As detailed in Sections  and , the earliest style transfer algorithms were based on rearranging and blending patches of pixels taken directly from the style image [1]}, [2]}. Several recent style transfer algorithms [3]}, [4]}, [5]} took inspiration from this approach, rearranging neural features extracted from the style image, then recovering an RGB image from the resulting tensor. However, based on our user study when evaluating STROTSS (see Figure REF ), these methods do not actually improve over the original style transfer formulation proposed by Gatys et al. [6]}. NNST is not based on any fundamentally new ideas, at the end of the day it also rearranges neural features and decodes an image from the resulting tensor; instead it outlines a set of important design decisions that dramatically improve outputs' visual quality. The paper this chapter is based on is currently under review, and was joint work with Michal Kučera, Daniel Sýkora, Eli Shechtman, Sylvain Paris, and my advisor Greg Shakhnarovich.
